Circuits and Systems for Embodied AI: Exploring uJ Multi-Modal Perception for Nano-UAVs on the Kraken Shield

Embodied AI requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicles. On small form-factor devices, e.g., nano-UAVs, such challenges are exacerbated by stringent constraints on energy efficiency and weight. In this paper, we explore embodied multi-modal AI-based perception for Nano-UAVs with the Kraken shield, a 7g multi-sensor (frame-based and event-based imagers) board based on Kraken, a 22 nm SoC featuring multiple acceleration engines for multi-modal event and frame-based inference based on spiking (SNN) and ternary (TNN) neural networks, respectively. Kraken can execute SNN real-time inference for depth estimation at 1.02 k inf/s, $18 \ \mu \mathrm{J} / \mathrm{inf}$, TNN real-time inference for object classification at 10 k inf/s, $6 \mu \ \mathrm{J} / \mathrm{inf}$, and real-time inference for obstacle avoidance at 221 frame/s, $750 \ \mu \mathrm{J} /$inf.